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Network Traffic Forensics on Firefox Mobile OS

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Network Traffic Forensics on Firefox Mobile OS This is authors accepted copy, you may find final version of the paper at: Mohd Najwadi Yusoff, Ali Dehghantanha, Ramlan Mahmod, “Network Traffic Forensics on Firefox Mobile OS: Facebook, Twitter, and Telegram as Case Studies” Pages 63-78, Chapter 5, (Elsevier) Contemporary Digital Forensic Investigations Of Cloud And Mobile Applications Network Traffic Forensics on Firefox Mobile OS: Facebook, Twitter and Telegram as Case Studies Mohd Najwadi Yusoff School of Computer Science, Universiti Sains Malaysia, Penang, Malaysia. [email protected] Ali Dehghantanha School of Computing, Science and Engineering, University of Salford, Manchester, United Kingdom. [email protected] Ramlan Mahmod Faculty of Computer Science and Information Technology, Universiti Putra Malaysia, Serdang, Malaysia. [email protected] Abstract Development of mobile web-centric OS such as Firefox OS has created new challenges, and opportunities for digital investigators. Network traffic forensic plays an important role in cybercrime investigation to detect subject(s) and object(s) of the crime. In this chapter, we detect and analyze residual network traffic artefacts of Firefox OS in relation to two popular social networking applications (Facebook and Twitter) and one instant messaging application (Telegram). We utilized a Firefox OS simulator to generate relevant traffic while all communication data were captured using network monitoring tools. Captured network packets were examined and remnants with forensic value were reported. This paper as the first focused study on mobile Firefox OS network traffic analysis should pave the way for the future research in this direction. Keywords: Firefox OS; digital forensics; mobile forensics; network monitoring; network traffic; virtual environment 1. Introduction The significant rise of social media networking, instant messaging platform, webmail and other mobile web applications has spawned the idea to build mobile web-centric operating systems (OS) using different open web standards like HTML5, CSS3, and JavaScript. Due to that fact, Mozilla has released the world’s first mobile web-centric OS on February 21st, 2013. This mobile web-centric OS is based on Firefox web browser rendering engine on top of Linux kernel, called Firefox OS (FxOS) (Mozilla Corporation, 2013). The emergence of mobile web- centric OS has created new challenges, concentrations and opportunities for digital investigators. In general, the growth of mobile devices may allow cybercriminals to utilize social media and instant messaging services for malicious purposes (Mohtasebi and Dehghantanha, 2011) such as spreading malicious codes, obtaining and disseminating confidential information etc. Furthermore, copyright infringement, cyber stalking, cyber bullying, slander spreading and sexual harassment are becoming serious threats to mobile device users (Dezfouli et al., 2013). Therefore, it is common to confront with different types of mobile devices during variety of forensics investigation cases (Damshenas et al., 2014). Many previous studies were focused on detection and analysis of network traffic artefacts for cyber forensics. Quick and Choo run Dropbox cloud storage in virtual environment machine and all network activities were recorded (Quick and Choo, 2013a). Network Miner 1.0 (Hjelmvik, 2014) and Wireshark Portable 1.6.5 (Combs, 2013) were used to capture the network traffic in many circumstances and network traffic was seen on TCP port 80 and 443 only. Quick and Choo also run Microsoft SkyDrive in virtual environment machine using the same method (Quick and Choo, 2013b). The result were tabled with more information such as IP start, IP finish, URL observed in network traffic and registered owner. No username and password were observed in the clear text network traffic. Quick and Choo continued the research and run Google Drive cloud storage as the case study (Quick and Choo, 2014). Google Drive account credential was observed but cannot be seen in the network traffic, suggesting the data was encrypted. Martini and Choo observed network traffic from virtual environment network adapter using ownCloud as a case study (Martini and Choo, 2013). HTTP Basic authentications were captured and user’s ownCloud credentials were successfully displayed. Farina produced an analysis of the sequence of network traffic and file I/O interactions in the torrent synchronization process (Farina et al., 2014). Bittorrent client were used as the test subject. Utilizing virtual machines for generating network traffic is a very common in forensics research. Blakeley investigate cloud storage software using hubiC as a case study (Blakeley et al., 2015). In the network analysis part, it was observed that a redirect to HTTPS on port 8080 was returned when the initial request was made to www.hubic.com. This shows that there was no plaintext traffic accepted by the hubiC website. Shariati run network analysis using SugarSync cloud storage (Shariati et al., 2015). Majority of communication were encrypted and no credentials or contents of sample data-set were able to be recovered. Dezfouli investigated Facebook, Twitter, LinkedIn and Google+ artefacts on Android and iOS. During experiment, network activities were captured and able to determine user’s IP address, domain name of connected social media sites and corresponding session timestamps. Yang identify network artefacts of Facebook and Skype Windows Store application (Yang et al., 2016). Yang was able to correlate the IP addresses with the timestamp information to determine when the application was started up and the duration of application used during experiment. Daryabar investigated OneDrive, Box, Google Drive, Dropbox applications on Android and iOS devices (Daryabar et al., 2016a). In network analysis part, the connection were secured between cloud client application and the server, thus no credential were able to be seen. Daryabar also investigated the MEGA cloud client application on Android and iOS (Daryabar et al., 2016b). Daryabar identify network artefact arising from user activities, such as login, uploading, downloading, deletion, and file sharing including timestamps. Table 1 is reflecting literature summary of network analysis and monitoring captured through virtual environment network adapter. Table 1 - Summary of Network Analysis and Monitoring Captured through Virtual Environment Network Adapter Researcher(s) Application(s) Application Type Martini and Choo 2013 OwnCloud Cloud Storage Quick and Choo 2013b Dropbox Cloud Storage Quick and Choo 2013c Microsoft SkyDrive Cloud Storage Quick and Choo 2014 Google Drive Cloud Storage Farina et al. 2014 Bittorrent Torrent Client Blakeley et al. 2015 hubiC Cloud Storage Shariati et al. 2015 SugarSync Cloud Storage Dezfouli et al. 2015 Facebook, Twitter, Social Media LinkdIn, Google + Yang et al. 2016 Facebook, Social Media Skype VoIP Daryabar et al. 2016 OneDrive, Box, Google Cloud Storage Drive, Dropbox Daryabar et al. 2016 MEGA Cloud Storage As can be seen from Table 1, there was no previous analyzing FxOS network traffic artefacts. FxOS is designed to allow mobile devices to communicate directly with HTML5 applications using JavaScript and newly introduced WebAPI. However, the used of JavaScript in HTML5 applications and solely no OS restriction might lead to security issues and further potential exploits and threats. FxOS is still not fully supported by most of the existing mobile forensic tools which further urgencies for further research development in this area (Yusoff et al., 2014a). In this chapter, we were focused on analysis of residual network traffic artefacts in FxOS. Two popular social networking applications (Facebook and Twitter) and one instant messaging application (Telegram) were investigated as case studies. In the earliest days, investigators used to put the mobile phone in the special sandbox to monitor mobile GSM activities (Androulidakis, 2012). This method however, requires a lot of expensive devices and thus, the phone monitoring process became more complicated and very costly. FxOS simulator is a virtualised version of the FxOS that runs provides full user experience and FxOS features. Therefore, we have followed the method proposed by Quick and Choo and simulated FxOS in a virtual environment machine (Quick and Choo, 2013c). When we performed the communication activities using the FxOS simulator, we used the Network Analyzer to capture and monitor the network traffic. The rest of this chapter is organized as follows. In section 2, we have explained the methodology used and outlined the setup for our experiment. In section 3, we have presented our research findings and finally concluded our research in section 4. 2. Experiment Setup Network analysis is a procedure for investigating the movement of data that travel across the targeted network. This procedure was performed by analyzing and carving the captured network artefacts. In general, the collection of network artefact for mobile devices is very difficult because of the limitation of mobile hardware and mobile OS itself. It is in contrary with conventional desktop OS, which we can easily captured the network files using various tools available. For that reason alone, we have adapted an approach for forensic collection of cloud artefacts (Quick and Choo, 2013b, 2013c, 2014) into our research methodology. Quick and Choo analysed several cloud applications using virtual machines and captured and analysed network traffic activities. In this chapter, we run FxOS
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